Veena et al [1]} introduced five different spatial LSB algorithms: LSB Replacement (LSBR), LSB Matching (LSBM), LSBM Revisited (LSBMR), Two bit LSBR (LSBR2) and Modulo 5 LSBR (LSBRmod5). The authors applied GRASP-BGWO and observed that the detection process is highly dependent on three important properties: training algorithms, payloads and features. According to their research study, GRASP-BGWO excelled all existing works like SRM [2]} (Spatial Rich Mode), PSRM (Projected SRM) [3]} and SPAM, even in low volume payload per pixel. GRASP-BGWO improved performance up to 13 percent. In addition to the given GRASP-BGWO and it's performance on LSBR, Shojae Chae-ikar and Ashmadi [4]} presented a novel ensemble Similarity weight (SW)-based image steganalysis. The ensemble SW steganalysis comprises three main steps. First is SW analysis, the second step is to adopt SVM classifier, and the third step makes a decision of stego vs cover. In SW analysis, the researchers compute the pixel and channel similarity weights of the given object and generate PSW and CSW datasets. Then, they compare the datasets with their corresponding reference profiles. The last step takes the generated datasets from the second step to make the final decision on whether the image is stego or cover.
